This Application claims the benefit of U.S. provisional Application No. 61/633,246, entitled TOOLS AND METHODS FOR DETERMINING RELATIONSHIP VALUES, filed Feb. 8, 2012.
1. Technical Field
The present invention relates to characteristic-based profiling systems and, more particularly, to combining multiple points of data regarding individuals through the use of characteristics in order to determine the relationship between the individuals and a user-defined criteria.
2. Description of the Related Art
Customer profiling systems are known in the art. Traditional systems include consumer rewards cards, credit card purchase information, demographic profiling, behavioral profiling, and customer surveying. Some businesses supplement these traditional systems with website and social media analytic tools that profile the business's fans and followers according to factors such as “likes,” “click-through rates,” and search engine queries, among others. Generally, these systems attempt to determine products, promotions, and advertisements that are most likely to appeal to a specific customer or broad customer segment. This information helps businesses forecast future market behavior, manage their product portfolio and inventory levels, adjust product pricing, design marketing strategies, and determine human resource and capital investment needs in order to increase revenue, market share, and profitability. For example, advertising targeted at customers who are most likely to purchase a product may be more effective than advertising targeting broader audiences. Likewise, products that are related to one another are likely to be purchased by the same customer and may sell better if offered at the same time, whether as a package or as separate items. Online retailers often use a similar approach, suggesting items that other customers frequently purchase in conjunction with the selected item.
While the prior art approaches create basic customer profiles, these profiles do not reflect the myriad similarities between customers or the numerous ways in which customers can be grouped. For example, the prior art approaches generally provide profiles on either an individual customer or an overly broad customer segment (for example, all women ages 25-34 with a college degree), failing to reflect the various degrees of granularity with which customers can be grouped.
One type of prior art approach typically uses only historical, static, and quantitative or objective information. As a result, customer profiles created by these prior art approaches are generally outdated and inaccurate, and fail to account for the vast amount of potentially rich, but qualitative and subjective, information about the customer that is available to most businesses.
A second type of prior art approach uses only subjective or qualitative information. These approaches also have drawbacks. Typically they use expensive and time-consuming methods such as customer surveys or focus groups. Due to the nature of the setting, the results may not accurately reflect the attitudes or opinions of the surveyed individuals. Due to the expense and time involved, only a limited number of individuals may be surveyed.
Additionally, customer information is often collected with respect to a single business metric and may never be used to glean insights about other metrics that may be helpful to the company. This is particularly true for businesses that are growing and those that have multiple departments. Growing businesses must usually adjust or supplement their performance metrics to reflect new goals, strategies, and business operations. As a result, these businesses must understand how their customers relate to the new set of business metrics rather than, or in addition to, the ones for which the data was originally collected. Similarly, businesses with multiple departments frequently gather customer information for purposes of a department-specific metric, but fail to use that information across other departments or globally within the organization. For example, a business may have a marketing department and risk management department. Customer information gathered by the marketing department when researching new product markets may never be seen or used by the risk management team to determine whether that customer or market poses undue risk to the business. Prior art methods for combining this disparate data, (for example, a technique sometimes referred to as “one version of the truth analysis”) do not allow the business to apply the same method to external data it may be interested in. Furthermore, these prior art systems are used only to organize the information and are not useful for analyzing it.
As a result, there is a need for a system that addresses the issues above.